{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"}],"enrichment":{"capability":"Wraps scikit-learn estimators to return pandas DataFrames instead of numpy arrays, preserving feature names through transformations and enabling feature traceability across pipelines.","skillfed_tags":["scikit-learn-wrapper","feature-tracing","dataframe-preservation"],"use_cases":["Inspect and debug scikit-learn pipelines by keeping track of feature names through all transformation steps.","One-hot encode categorical features while maintaining a clear mapping from encoded columns back to original features.","Audit feature engineering in production models to understand which raw inputs drive each final feature.","Build interpretable machine learning workflows where feature lineage is essential for model validation.","Integrate scikit-learn into data analysis notebooks where preserving DataFrame structure simplifies downstream exploration."],"what_it_does":"sklearndf is a wrapper library that enhances scikit-learn estimators to preserve pandas DataFrames and feature names through transformations. When you use scikit-learn's transformers, they typically return numpy arrays even if your input was a DataFrame, losing column names in the process. This makes it hard to trace which features went where, especially in complex pipelines with feature engineering steps. sklearndf solves this by providing drop-in replacements (e.g., StandardScalerDF instead of StandardScaler) that return DataFrames with feature names intact.\n\nThe library depends on numpy, pandas, scipy, scikit-learn, packaging, and gamma-pytools. It supports Python 3.9 through 3.13 and is marked Production/Stable. The main value is in model inspection and debugging: you can see exactly which original features contributed to each output feature, which is crucial when transformers create new features (like one-hot encoding) or when you need to audit a pipeline's behavior.","worth_installing":"Yes, if you work regularly with scikit-learn pipelines and need to preserve feature names and traceability. The library is stable, has low install friction, and solves a genuine pain point in scikit-learn workflows. Maintenance is aging (last release 345 days ago), so verify compatibility with your scikit-learn version before adopting in new projects. No known security vulnerabilities."},"id":"sklearndf","links":{"html":"https://skillfed.io/packages/sklearndf","md":"https://skillfed.io/packages/sklearndf.md","pypi":"https://pypi.org/project/sklearndf/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-09-03","license_spdx":null,"license_treatment":"permissive","name":"sklearndf","python_support":"supports_current","summary":"Data frame support and feature traceability for `scikit-learn`."},"popularity":{"monthly_downloads":85647,"position":13910,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.4.2"}